Overview

Dataset statistics

Number of variables21
Number of observations21253
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 MiB
Average record size in memory200.9 B

Variable types

Numeric16
Categorical4
Boolean1

Alerts

주소 has a high cardinality: 399 distinct valuesHigh cardinality
시간단위기온 is highly overall correlated with 분기구분High correlation
시간단위습도 is highly overall correlated with 평균 상대습도(%) and 1 other fieldsHigh correlation
평균 상대습도(%) is highly overall correlated with 시간단위습도 and 1 other fieldsHigh correlation
실효습도 is highly overall correlated with 시간단위습도 and 1 other fieldsHigh correlation
전기사용량(KWh) is highly overall correlated with 가스사용량(KWh)High correlation
가스사용량(KWh) is highly overall correlated with 전기사용량(KWh)High correlation
분기구분 is highly overall correlated with 시간단위기온High correlation
발화요인대분류명 is highly imbalanced (51.3%)Imbalance
사망인명피해수 is highly skewed (γ1 = 30.96680039)Skewed
현장소방지역대거리 is highly skewed (γ1 = 20.38416342)Skewed
재산피해액 is highly skewed (γ1 = 142.6365673)Skewed
사망인명피해수 has 20289 (95.5%) zerosZeros
화재발생시 has 841 (4.0%) zerosZeros
현장소방서거리 has 221 (1.0%) zerosZeros
현장안전센터거리 has 945 (4.4%) zerosZeros
현장소방지역대거리 has 21122 (99.4%) zerosZeros
시간단위풍향 has 621 (2.9%) zerosZeros
재산피해액 has 342 (1.6%) zerosZeros

Reproduction

Analysis started2023-05-04 14:30:28.503471
Analysis finished2023-05-04 14:31:09.357794
Duration40.85 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

사망인명피해수
Real number (ℝ)

SKEWED  ZEROS 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.068696184
Minimum0
Maximum33
Zeros20289
Zeros (%)95.5%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:09.474014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum33
Range33
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.51733108
Coefficient of variation (CV)7.5307106
Kurtosis1624.5576
Mean0.068696184
Median Absolute Deviation (MAD)0
Skewness30.9668
Sum1460
Variance0.26763145
MonotonicityNot monotonic
2023-05-04T23:31:09.582593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 20289
95.5%
1 751
 
3.5%
2 120
 
0.6%
3 45
 
0.2%
4 23
 
0.1%
5 11
 
0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
10 2
 
< 0.1%
11 1
 
< 0.1%
Other values (7) 7
 
< 0.1%
ValueCountFrequency (%)
0 20289
95.5%
1 751
 
3.5%
2 120
 
0.6%
3 45
 
0.2%
4 23
 
0.1%
5 11
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
33 1
< 0.1%
32 1
< 0.1%
18 1
< 0.1%
15 1
< 0.1%
14 1
< 0.1%
11 1
< 0.1%
10 2
< 0.1%
9 1
< 0.1%
8 2
< 0.1%
7 1
< 0.1%

분기구분
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size848.1 KiB
3
5596 
1
5377 
4
5176 
2
5104 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21253
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 5596
26.3%
1 5377
25.3%
4 5176
24.4%
2 5104
24.0%

Length

2023-05-04T23:31:09.693360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-04T23:31:09.815013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
3 5596
26.3%
1 5377
25.3%
4 5176
24.4%
2 5104
24.0%

Most occurring characters

ValueCountFrequency (%)
3 5596
26.3%
1 5377
25.3%
4 5176
24.4%
2 5104
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21253
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 5596
26.3%
1 5377
25.3%
4 5176
24.4%
2 5104
24.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21253
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 5596
26.3%
1 5377
25.3%
4 5176
24.4%
2 5104
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21253
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 5596
26.3%
1 5377
25.3%
4 5176
24.4%
2 5104
24.0%

화재발생일자
Real number (ℝ)

Distinct1826
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20189215
Minimum20170101
Maximum20211231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:09.953444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum20170101
5-th percentile20170328
Q120180215
median20190331
Q320200726
95-th percentile20210917
Maximum20211231
Range41130
Interquartile range (IQR)20511

Descriptive statistics

Standard deviation13904.782
Coefficient of variation (CV)0.00068872325
Kurtosis-1.2305931
Mean20189215
Median Absolute Deviation (MAD)10215
Skewness0.1635452
Sum4.2908138 × 1011
Variance1.9334295 × 108
MonotonicityNot monotonic
2023-05-04T23:31:10.102275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20180126 36
 
0.2%
20180128 30
 
0.1%
20190507 29
 
0.1%
20180205 27
 
0.1%
20180801 27
 
0.1%
20190128 26
 
0.1%
20180210 25
 
0.1%
20170502 24
 
0.1%
20170731 24
 
0.1%
20180112 24
 
0.1%
Other values (1816) 20981
98.7%
ValueCountFrequency (%)
20170101 9
< 0.1%
20170102 8
< 0.1%
20170103 6
 
< 0.1%
20170104 6
 
< 0.1%
20170105 8
< 0.1%
20170106 12
0.1%
20170107 14
0.1%
20170108 8
< 0.1%
20170109 19
0.1%
20170110 10
< 0.1%
ValueCountFrequency (%)
20211231 9
< 0.1%
20211230 13
0.1%
20211229 14
0.1%
20211228 13
0.1%
20211227 22
0.1%
20211226 17
0.1%
20211225 14
0.1%
20211224 5
 
< 0.1%
20211223 10
< 0.1%
20211222 15
0.1%

화재발생시
Real number (ℝ)

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.768174
Minimum0
Maximum23
Zeros841
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:10.221944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median13
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.6001129
Coefficient of variation (CV)0.5169191
Kurtosis-0.90114411
Mean12.768174
Median Absolute Deviation (MAD)5
Skewness-0.34147258
Sum271362
Variance43.56149
MonotonicityNot monotonic
2023-05-04T23:31:10.337021image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 1213
 
5.7%
19 1175
 
5.5%
18 1127
 
5.3%
13 1117
 
5.3%
12 1115
 
5.2%
14 1097
 
5.2%
20 1094
 
5.1%
11 1087
 
5.1%
16 1065
 
5.0%
17 1024
 
4.8%
Other values (14) 10139
47.7%
ValueCountFrequency (%)
0 841
4.0%
1 745
3.5%
2 670
3.2%
3 568
2.7%
4 532
2.5%
5 422
2.0%
6 477
2.2%
7 526
2.5%
8 710
3.3%
9 880
4.1%
ValueCountFrequency (%)
23 895
4.2%
22 912
4.3%
21 962
4.5%
20 1094
5.1%
19 1175
5.5%
18 1127
5.3%
17 1024
4.8%
16 1065
5.0%
15 1213
5.7%
14 1097
5.2%

현장소방서거리
Real number (ℝ)

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2178516
Minimum0
Maximum20
Zeros221
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:10.449800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7286972
Coefficient of variation (CV)0.53722091
Kurtosis2.4192214
Mean3.2178516
Median Absolute Deviation (MAD)1
Skewness1.0613722
Sum68389
Variance2.9883939
MonotonicityNot monotonic
2023-05-04T23:31:10.560047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 5369
25.3%
2 4937
23.2%
4 3860
18.2%
1 2810
13.2%
5 2037
 
9.6%
6 1010
 
4.8%
7 515
 
2.4%
8 297
 
1.4%
0 221
 
1.0%
9 122
 
0.6%
Other values (6) 75
 
0.4%
ValueCountFrequency (%)
0 221
 
1.0%
1 2810
13.2%
2 4937
23.2%
3 5369
25.3%
4 3860
18.2%
5 2037
 
9.6%
6 1010
 
4.8%
7 515
 
2.4%
8 297
 
1.4%
9 122
 
0.6%
ValueCountFrequency (%)
20 2
 
< 0.1%
14 4
 
< 0.1%
13 3
 
< 0.1%
12 8
 
< 0.1%
11 23
 
0.1%
10 35
 
0.2%
9 122
 
0.6%
8 297
 
1.4%
7 515
2.4%
6 1010
4.8%

현장안전센터거리
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6217475
Minimum0
Maximum10
Zeros945
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:10.679439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88375313
Coefficient of variation (CV)0.54493879
Kurtosis3.9174881
Mean1.6217475
Median Absolute Deviation (MAD)1
Skewness1.1838632
Sum34467
Variance0.78101959
MonotonicityNot monotonic
2023-05-04T23:31:10.779329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 9796
46.1%
2 7684
36.2%
3 2245
 
10.6%
0 945
 
4.4%
4 438
 
2.1%
5 91
 
0.4%
6 33
 
0.2%
7 12
 
0.1%
8 4
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
0 945
 
4.4%
1 9796
46.1%
2 7684
36.2%
3 2245
 
10.6%
4 438
 
2.1%
5 91
 
0.4%
6 33
 
0.2%
7 12
 
0.1%
8 4
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 3
 
< 0.1%
8 4
 
< 0.1%
7 12
 
0.1%
6 33
 
0.2%
5 91
 
0.4%
4 438
 
2.1%
3 2245
 
10.6%
2 7684
36.2%
1 9796
46.1%

현장소방지역대거리
Real number (ℝ)

SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0091751753
Minimum0
Maximum6
Zeros21122
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:10.883221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.13602315
Coefficient of variation (CV)14.825129
Kurtosis529.19513
Mean0.0091751753
Median Absolute Deviation (MAD)0
Skewness20.384163
Sum195
Variance0.018502298
MonotonicityNot monotonic
2023-05-04T23:31:10.971591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 21122
99.4%
1 92
 
0.4%
2 22
 
0.1%
3 11
 
0.1%
4 5
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 21122
99.4%
1 92
 
0.4%
2 22
 
0.1%
3 11
 
0.1%
4 5
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
4 5
 
< 0.1%
3 11
 
0.1%
2 22
 
0.1%
1 92
 
0.4%
0 21122
99.4%
Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size848.1 KiB
부주의
12093 
전기적 요인
5654 
미상
1717 
기계적 요인
 
970
방화
 
224
Other values (7)
 
595

Length

Max length8
Median length3
Mean length3.8977556
Min length2

Characters and Unicode

Total characters82839
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전기적 요인
2nd row부주의
3rd row부주의
4th row부주의
5th row부주의

Common Values

ValueCountFrequency (%)
부주의 12093
56.9%
전기적 요인 5654
26.6%
미상 1717
 
8.1%
기계적 요인 970
 
4.6%
방화 224
 
1.1%
화학적 요인 180
 
0.8%
방화의심 169
 
0.8%
기타 92
 
0.4%
가스누출(폭발) 75
 
0.4%
제품결함 51
 
0.2%
Other values (2) 28
 
0.1%

Length

2023-05-04T23:31:11.087525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부주의 12093
43.1%
요인 6830
24.3%
전기적 5654
20.1%
미상 1717
 
6.1%
기계적 970
 
3.5%
방화 224
 
0.8%
화학적 180
 
0.6%
방화의심 169
 
0.6%
기타 92
 
0.3%
가스누출(폭발 75
 
0.3%
Other values (3) 79
 
0.3%

Most occurring characters

ValueCountFrequency (%)
12262
14.8%
12093
14.6%
12093
14.6%
6856
8.3%
6830
8.2%
6830
8.2%
6830
8.2%
6716
8.1%
5654
6.8%
1717
 
2.1%
Other values (25) 4958
6.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75859
91.6%
Space Separator 6830
 
8.2%
Open Punctuation 75
 
0.1%
Close Punctuation 75
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12262
16.2%
12093
15.9%
12093
15.9%
6856
9.0%
6830
9.0%
6830
9.0%
6716
8.9%
5654
7.5%
1717
 
2.3%
1717
 
2.3%
Other values (22) 3091
 
4.1%
Space Separator
ValueCountFrequency (%)
6830
100.0%
Open Punctuation
ValueCountFrequency (%)
( 75
100.0%
Close Punctuation
ValueCountFrequency (%)
) 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 75859
91.6%
Common 6980
 
8.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12262
16.2%
12093
15.9%
12093
15.9%
6856
9.0%
6830
9.0%
6830
9.0%
6716
8.9%
5654
7.5%
1717
 
2.3%
1717
 
2.3%
Other values (22) 3091
 
4.1%
Common
ValueCountFrequency (%)
6830
97.9%
( 75
 
1.1%
) 75
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 75859
91.6%
ASCII 6980
 
8.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
12262
16.2%
12093
15.9%
12093
15.9%
6856
9.0%
6830
9.0%
6830
9.0%
6716
8.9%
5654
7.5%
1717
 
2.3%
1717
 
2.3%
Other values (22) 3091
 
4.1%
ASCII
ValueCountFrequency (%)
6830
97.9%
( 75
 
1.1%
) 75
 
1.1%
Distinct36
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size848.1 KiB
공동주택
6663 
단독주택
4595 
음식점
2943 
일반업무
1812 
일상서비스
1170 
Other values (31)
4070 

Length

Max length6
Median length4
Mean length3.9181763
Min length2

Characters and Unicode

Total characters83273
Distinct characters71
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row단독주택
2nd row일상서비스
3rd row음식점
4th row음식점
5th row단독주택

Common Values

ValueCountFrequency (%)
공동주택 6663
31.4%
단독주택 4595
21.6%
음식점 2943
13.8%
일반업무 1812
 
8.5%
일상서비스 1170
 
5.5%
판매시설 944
 
4.4%
작업장 469
 
2.2%
기타건축물 393
 
1.8%
숙박시설 225
 
1.1%
공장시설 215
 
1.0%
Other values (26) 1824
 
8.6%

Length

2023-05-04T23:31:11.211854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공동주택 6663
31.4%
단독주택 4595
21.6%
음식점 2943
13.8%
일반업무 1812
 
8.5%
일상서비스 1170
 
5.5%
판매시설 944
 
4.4%
작업장 469
 
2.2%
기타건축물 393
 
1.8%
숙박시설 225
 
1.1%
공장시설 215
 
1.0%
Other values (26) 1824
 
8.6%

Most occurring characters

ValueCountFrequency (%)
11392
13.7%
11392
13.7%
7047
 
8.5%
6888
 
8.3%
4595
 
5.5%
4595
 
5.5%
2982
 
3.6%
2947
 
3.5%
2943
 
3.5%
2943
 
3.5%
Other values (61) 25549
30.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83075
99.8%
Other Punctuation 198
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11392
13.7%
11392
13.7%
7047
 
8.5%
6888
 
8.3%
4595
 
5.5%
4595
 
5.5%
2982
 
3.6%
2947
 
3.5%
2943
 
3.5%
2943
 
3.5%
Other values (60) 25351
30.5%
Other Punctuation
ValueCountFrequency (%)
/ 198
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83075
99.8%
Common 198
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11392
13.7%
11392
13.7%
7047
 
8.5%
6888
 
8.3%
4595
 
5.5%
4595
 
5.5%
2982
 
3.6%
2947
 
3.5%
2943
 
3.5%
2943
 
3.5%
Other values (60) 25351
30.5%
Common
ValueCountFrequency (%)
/ 198
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83075
99.8%
ASCII 198
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
11392
13.7%
11392
13.7%
7047
 
8.5%
6888
 
8.3%
4595
 
5.5%
4595
 
5.5%
2982
 
3.6%
2947
 
3.5%
2943
 
3.5%
2943
 
3.5%
Other values (60) 25351
30.5%
ASCII
ValueCountFrequency (%)
/ 198
100.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size702.8 KiB
False
14672 
True
6581 
ValueCountFrequency (%)
False 14672
69.0%
True 6581
31.0%
2023-05-04T23:31:11.343358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

시간단위기온
Real number (ℝ)

Distinct547
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.837628
Minimum-17.9
Maximum39.4
Zeros51
Zeros (%)0.2%
Negative3037
Negative (%)14.3%
Memory size848.1 KiB
2023-05-04T23:31:11.456262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-17.9
5-th percentile-5.8
Q14.5
median15.2
Q323.6
95-th percentile30.2
Maximum39.4
Range57.3
Interquartile range (IQR)19.1

Descriptive statistics

Standard deviation11.574746
Coefficient of variation (CV)0.83646897
Kurtosis-0.89071966
Mean13.837628
Median Absolute Deviation (MAD)9.4
Skewness-0.28053188
Sum294091.1
Variance133.97475
MonotonicityNot monotonic
2023-05-04T23:31:11.590493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.6 100
 
0.5%
25.9 98
 
0.5%
25 98
 
0.5%
25.6 94
 
0.4%
22.7 93
 
0.4%
22.8 93
 
0.4%
25.2 92
 
0.4%
25.5 92
 
0.4%
24 91
 
0.4%
25.1 91
 
0.4%
Other values (537) 20311
95.6%
ValueCountFrequency (%)
-17.9 1
 
< 0.1%
-17.8 2
 
< 0.1%
-17.5 2
 
< 0.1%
-17.4 1
 
< 0.1%
-17.2 1
 
< 0.1%
-16.9 1
 
< 0.1%
-16.5 2
 
< 0.1%
-16.4 2
 
< 0.1%
-16.3 1
 
< 0.1%
-16.2 5
< 0.1%
ValueCountFrequency (%)
39.4 2
 
< 0.1%
39.3 1
 
< 0.1%
39 2
 
< 0.1%
38.7 4
< 0.1%
37.9 2
 
< 0.1%
37.8 2
 
< 0.1%
37.6 2
 
< 0.1%
37.4 5
< 0.1%
37.3 1
 
< 0.1%
37.2 2
 
< 0.1%

시간단위풍속
Real number (ℝ)

Distinct81
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2390345
Minimum0
Maximum9.1
Zeros63
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:11.733747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q11.3
median2.1
Q33
95-th percentile4.4
Maximum9.1
Range9.1
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.1833944
Coefficient of variation (CV)0.5285289
Kurtosis0.70351972
Mean2.2390345
Median Absolute Deviation (MAD)0.8
Skewness0.72816557
Sum47586.2
Variance1.4004224
MonotonicityNot monotonic
2023-05-04T23:31:11.875050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 777
 
3.7%
2.1 720
 
3.4%
1.7 710
 
3.3%
2.2 705
 
3.3%
1.8 704
 
3.3%
1.4 699
 
3.3%
1.6 698
 
3.3%
2.3 692
 
3.3%
1.1 685
 
3.2%
1.9 666
 
3.1%
Other values (71) 14197
66.8%
ValueCountFrequency (%)
0 63
 
0.3%
0.1 79
 
0.4%
0.2 92
 
0.4%
0.3 167
 
0.8%
0.4 220
 
1.0%
0.5 309
1.5%
0.6 370
1.7%
0.7 474
2.2%
0.8 454
2.1%
0.9 554
2.6%
ValueCountFrequency (%)
9.1 1
 
< 0.1%
8.1 1
 
< 0.1%
8 2
 
< 0.1%
7.9 2
 
< 0.1%
7.7 4
< 0.1%
7.6 4
< 0.1%
7.5 3
< 0.1%
7.4 7
< 0.1%
7.3 6
< 0.1%
7.1 4
< 0.1%

시간단위풍향
Real number (ℝ)

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.55526
Minimum0
Maximum360
Zeros621
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:11.993911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q190
median250
Q3290
95-th percentile340
Maximum360
Range360
Interquartile range (IQR)200

Descriptive statistics

Standard deviation107.37736
Coefficient of variation (CV)0.52750961
Kurtosis-1.1854817
Mean203.55526
Median Absolute Deviation (MAD)50
Skewness-0.53057521
Sum4326160
Variance11529.897
MonotonicityNot monotonic
2023-05-04T23:31:12.100539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
290 3545
16.7%
270 3505
16.5%
50 1900
8.9%
250 1560
7.3%
70 1547
7.3%
320 1518
7.1%
230 1138
 
5.4%
200 1100
 
5.2%
20 1025
 
4.8%
90 857
 
4.0%
Other values (7) 3558
16.7%
ValueCountFrequency (%)
0 621
 
2.9%
20 1025
4.8%
50 1900
8.9%
70 1547
7.3%
90 857
4.0%
110 528
 
2.5%
140 337
 
1.6%
160 276
 
1.3%
180 493
 
2.3%
200 1100
5.2%
ValueCountFrequency (%)
360 608
 
2.9%
340 695
 
3.3%
320 1518
7.1%
290 3545
16.7%
270 3505
16.5%
250 1560
7.3%
230 1138
 
5.4%
200 1100
 
5.2%
180 493
 
2.3%
160 276
 
1.3%

시간단위습도
Real number (ℝ)

Distinct93
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.729026
Minimum7
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:12.234736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile25
Q141
median55
Q373
95-th percentile93
Maximum100
Range93
Interquartile range (IQR)32

Descriptive statistics

Standard deviation20.720054
Coefficient of variation (CV)0.36524608
Kurtosis-0.84371658
Mean56.729026
Median Absolute Deviation (MAD)16
Skewness0.14821035
Sum1205662
Variance429.32065
MonotonicityNot monotonic
2023-05-04T23:31:12.399817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 412
 
1.9%
51 387
 
1.8%
54 385
 
1.8%
46 379
 
1.8%
45 378
 
1.8%
53 376
 
1.8%
56 369
 
1.7%
44 367
 
1.7%
49 360
 
1.7%
50 354
 
1.7%
Other values (83) 17486
82.3%
ValueCountFrequency (%)
7 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
11 10
 
< 0.1%
12 15
 
0.1%
13 25
0.1%
14 33
0.2%
15 45
0.2%
16 53
0.2%
17 42
0.2%
ValueCountFrequency (%)
100 45
 
0.2%
99 61
 
0.3%
98 99
0.5%
97 225
1.1%
96 222
1.0%
95 199
0.9%
94 138
0.6%
93 153
0.7%
92 153
0.7%
91 168
0.8%

시간단위가시거리
Real number (ℝ)

Distinct1883
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1517.8316
Minimum27
Maximum3351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:12.538465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile381
Q11067
median1834
Q32000
95-th percentile2000
Maximum3351
Range3324
Interquartile range (IQR)933

Descriptive statistics

Standard deviation576.64484
Coefficient of variation (CV)0.3799136
Kurtosis-0.63281815
Mean1517.8316
Median Absolute Deviation (MAD)166
Skewness-0.87821118
Sum32258474
Variance332519.28
MonotonicityNot monotonic
2023-05-04T23:31:12.680534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 7243
34.1%
1992 63
 
0.3%
1996 59
 
0.3%
1999 59
 
0.3%
1995 54
 
0.3%
1997 52
 
0.2%
1989 47
 
0.2%
1998 47
 
0.2%
1994 47
 
0.2%
1993 47
 
0.2%
Other values (1873) 13535
63.7%
ValueCountFrequency (%)
27 1
 
< 0.1%
33 1
 
< 0.1%
47 1
 
< 0.1%
50 2
< 0.1%
51 1
 
< 0.1%
54 1
 
< 0.1%
57 1
 
< 0.1%
60 2
< 0.1%
61 2
< 0.1%
63 3
< 0.1%
ValueCountFrequency (%)
3351 1
 
< 0.1%
2396 2
 
< 0.1%
2000 7243
34.1%
1999 59
 
0.3%
1998 47
 
0.2%
1997 52
 
0.2%
1996 59
 
0.3%
1995 54
 
0.3%
1994 47
 
0.2%
1993 47
 
0.2%

재산피해액
Real number (ℝ)

SKEWED  ZEROS 

Distinct4571
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7011.9904
Minimum0
Maximum71613336
Zeros342
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:12.828288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q131
median129
Q3658
95-th percentile11462.6
Maximum71613336
Range71613336
Interquartile range (IQR)627

Descriptive statistics

Standard deviation494997.28
Coefficient of variation (CV)70.592977
Kurtosis20611.404
Mean7011.9904
Median Absolute Deviation (MAD)118
Skewness142.63657
Sum1.4902583 × 108
Variance2.4502231 × 1011
MonotonicityNot monotonic
2023-05-04T23:31:12.970538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 845
 
4.0%
20 558
 
2.6%
30 556
 
2.6%
50 451
 
2.1%
0 342
 
1.6%
55 273
 
1.3%
9 258
 
1.2%
33 245
 
1.2%
100 241
 
1.1%
22 217
 
1.0%
Other values (4561) 17267
81.2%
ValueCountFrequency (%)
0 342
1.6%
1 83
 
0.4%
2 66
 
0.3%
3 53
 
0.2%
4 64
 
0.3%
5 169
0.8%
6 67
 
0.3%
7 82
 
0.4%
8 134
 
0.6%
9 258
1.2%
ValueCountFrequency (%)
71613336 1
< 0.1%
7512359 1
< 0.1%
2432471 1
< 0.1%
2397753 1
< 0.1%
1586332 1
< 0.1%
1451930 1
< 0.1%
862341 1
< 0.1%
765935 1
< 0.1%
609230 1
< 0.1%
507760 1
< 0.1%

평균 상대습도(%)
Real number (ℝ)

Distinct510
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.137637
Minimum17.9
Maximum98.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:13.131346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum17.9
5-th percentile34.9
Q147.6
median59
Q369.8
95-th percentile86.8
Maximum98.1
Range80.2
Interquartile range (IQR)22.2

Descriptive statistics

Standard deviation15.592323
Coefficient of variation (CV)0.26366159
Kurtosis-0.47016625
Mean59.137637
Median Absolute Deviation (MAD)11
Skewness0.1820789
Sum1256852.2
Variance243.12055
MonotonicityNot monotonic
2023-05-04T23:31:13.280565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.1 159
 
0.7%
59.6 132
 
0.6%
64.4 132
 
0.6%
71.6 124
 
0.6%
43.1 122
 
0.6%
66.9 121
 
0.6%
54.4 107
 
0.5%
46.8 107
 
0.5%
51.3 105
 
0.5%
48.1 103
 
0.5%
Other values (500) 20041
94.3%
ValueCountFrequency (%)
17.9 13
 
0.1%
21.8 19
0.1%
22.9 12
 
0.1%
24.3 14
0.1%
25.5 12
 
0.1%
25.6 33
0.2%
25.8 17
0.1%
26.3 8
 
< 0.1%
26.4 18
0.1%
26.6 13
 
0.1%
ValueCountFrequency (%)
98.1 9
 
< 0.1%
97.6 14
0.1%
97 24
0.1%
96.8 13
 
0.1%
96.5 15
0.1%
96.3 34
0.2%
96 8
 
< 0.1%
95.8 26
0.1%
95.6 15
0.1%
95.5 14
0.1%

실효습도
Real number (ℝ)

Distinct1826
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.38442
Minimum23.940249
Maximum77.153859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:13.451321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum23.940249
5-th percentile32.92941
Q142.042174
median49.102314
Q355.983714
95-th percentile66.892989
Maximum77.153859
Range53.21361
Interquartile range (IQR)13.94154

Descriptive statistics

Standard deviation10.210708
Coefficient of variation (CV)0.20675971
Kurtosis-0.36172337
Mean49.38442
Median Absolute Deviation (MAD)7.002201
Skewness0.18367103
Sum1049567.1
Variance104.25857
MonotonicityNot monotonic
2023-05-04T23:31:13.601211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30.352695 36
 
0.2%
29.414988 30
 
0.1%
27.831885 29
 
0.1%
35.272713 27
 
0.1%
45.682773 27
 
0.1%
33.041187 26
 
0.1%
36.528615 25
 
0.1%
40.465089 24
 
0.1%
62.821065 24
 
0.1%
38.572617 24
 
0.1%
Other values (1816) 20981
98.7%
ValueCountFrequency (%)
23.940249 14
0.1%
25.89543 11
 
0.1%
25.94697 13
0.1%
27.498405 11
 
0.1%
27.561894 14
0.1%
27.831885 29
0.1%
27.834822 18
0.1%
27.936045 7
 
< 0.1%
27.971715 16
0.1%
28.007877 8
 
< 0.1%
ValueCountFrequency (%)
77.153859 21
0.1%
77.061375 19
0.1%
76.995897 11
0.1%
76.421967 13
0.1%
76.363509 20
0.1%
76.137183 17
0.1%
76.132254 14
0.1%
75.716724 15
0.1%
75.012003 17
0.1%
74.742159 9
< 0.1%

주소
Categorical

Distinct399
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size848.1 KiB
관악구 신림동
 
637
강서구 화곡동
 
518
관악구 봉천동
 
508
구로구 구로동
 
400
강남구 역삼동
 
374
Other values (394)
18816 

Length

Max length11
Median length7
Mean length7.238931
Min length5

Characters and Unicode

Total characters153849
Distinct characters203
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.1%

Sample

1st row강남구 논현동
2nd row강남구 논현동
3rd row강남구 논현동
4th row강남구 논현동
5th row강남구 논현동

Common Values

ValueCountFrequency (%)
관악구 신림동 637
 
3.0%
강서구 화곡동 518
 
2.4%
관악구 봉천동 508
 
2.4%
구로구 구로동 400
 
1.9%
강남구 역삼동 374
 
1.8%
노원구 상계동 342
 
1.6%
중랑구 면목동 340
 
1.6%
강북구 수유동 297
 
1.4%
강남구 논현동 296
 
1.4%
서초구 서초동 280
 
1.3%
Other values (389) 17261
81.2%

Length

2023-05-04T23:31:13.732242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 1547
 
3.6%
관악구 1187
 
2.8%
강서구 1163
 
2.7%
송파구 1053
 
2.5%
강동구 971
 
2.3%
중랑구 914
 
2.2%
서초구 889
 
2.1%
마포구 887
 
2.1%
서대문구 886
 
2.1%
구로구 872
 
2.1%
Other values (412) 32137
75.6%

Most occurring characters

ValueCountFrequency (%)
24229
 
15.7%
22729
 
14.8%
21253
 
13.8%
4482
 
2.9%
3424
 
2.2%
2406
 
1.6%
2307
 
1.5%
2252
 
1.5%
2221
 
1.4%
2210
 
1.4%
Other values (193) 66336
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 131001
85.1%
Space Separator 21253
 
13.8%
Decimal Number 1595
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
24229
 
18.5%
22729
 
17.4%
4482
 
3.4%
3424
 
2.6%
2406
 
1.8%
2307
 
1.8%
2252
 
1.7%
2221
 
1.7%
2210
 
1.7%
2172
 
1.7%
Other values (184) 62569
47.8%
Decimal Number
ValueCountFrequency (%)
2 503
31.5%
1 407
25.5%
3 317
19.9%
4 161
 
10.1%
5 133
 
8.3%
6 46
 
2.9%
7 24
 
1.5%
8 4
 
0.3%
Space Separator
ValueCountFrequency (%)
21253
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 131001
85.1%
Common 22848
 
14.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
24229
 
18.5%
22729
 
17.4%
4482
 
3.4%
3424
 
2.6%
2406
 
1.8%
2307
 
1.8%
2252
 
1.7%
2221
 
1.7%
2210
 
1.7%
2172
 
1.7%
Other values (184) 62569
47.8%
Common
ValueCountFrequency (%)
21253
93.0%
2 503
 
2.2%
1 407
 
1.8%
3 317
 
1.4%
4 161
 
0.7%
5 133
 
0.6%
6 46
 
0.2%
7 24
 
0.1%
8 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 131001
85.1%
ASCII 22848
 
14.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
24229
 
18.5%
22729
 
17.4%
4482
 
3.4%
3424
 
2.6%
2406
 
1.8%
2307
 
1.8%
2252
 
1.7%
2221
 
1.7%
2210
 
1.7%
2172
 
1.7%
Other values (184) 62569
47.8%
ASCII
ValueCountFrequency (%)
21253
93.0%
2 503
 
2.2%
1 407
 
1.8%
3 317
 
1.4%
4 161
 
0.7%
5 133
 
0.6%
6 46
 
0.2%
7 24
 
0.1%
8 4
 
< 0.1%

전기사용량(KWh)
Real number (ℝ)

Distinct399
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15736.438
Minimum864.475
Maximum59936.133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:13.890809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum864.475
5-th percentile4893.4262
Q18464.798
median11991.304
Q318727.654
95-th percentile40140.959
Maximum59936.133
Range59071.658
Interquartile range (IQR)10262.856

Descriptive statistics

Standard deviation11481.009
Coefficient of variation (CV)0.72958118
Kurtosis1.7913034
Mean15736.438
Median Absolute Deviation (MAD)4661.5693
Skewness1.5541609
Sum3.3444652 × 108
Variance1.3181357 × 108
MonotonicityNot monotonic
2023-05-04T23:31:14.074239image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8464.798003 637
 
3.0%
8526.846319 518
 
2.4%
12122.60555 508
 
2.4%
38593.10369 400
 
1.9%
27981.56536 374
 
1.8%
22353.13505 342
 
1.6%
6053.175667 340
 
1.6%
6579.535144 297
 
1.4%
18096.21046 296
 
1.4%
35154.32723 280
 
1.3%
Other values (389) 17261
81.2%
ValueCountFrequency (%)
864.475 1
 
< 0.1%
1360.270073 2
 
< 0.1%
1404.524609 4
< 0.1%
1486.1 3
< 0.1%
1508.576577 3
< 0.1%
1595.099346 1
 
< 0.1%
1841.104084 5
< 0.1%
1985.765509 2
 
< 0.1%
2048.809179 1
 
< 0.1%
2076.890208 1
 
< 0.1%
ValueCountFrequency (%)
59936.13307 11
 
0.1%
58207.94444 27
 
0.1%
57865.57723 7
 
< 0.1%
55019.1058 34
 
0.2%
54223.6424 6
 
< 0.1%
52495.57923 187
0.9%
52067.30813 91
0.4%
50761.05645 1
 
< 0.1%
50481.5704 12
 
0.1%
50268.83982 19
 
0.1%

가스사용량(KWh)
Real number (ℝ)

Distinct399
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20501.702
Minimum339.91667
Maximum127523.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size848.1 KiB
2023-05-04T23:31:14.221406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum339.91667
5-th percentile6131.0336
Q110114.289
median15159.201
Q322934.752
95-th percentile56661.654
Maximum127523.47
Range127183.55
Interquartile range (IQR)12820.463

Descriptive statistics

Standard deviation17838.813
Coefficient of variation (CV)0.87011376
Kurtosis9.8579307
Mean20501.702
Median Absolute Deviation (MAD)6244.6894
Skewness2.8636214
Sum4.3572267 × 108
Variance3.1822324 × 108
MonotonicityNot monotonic
2023-05-04T23:31:14.413298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13634.08214 637
 
3.0%
8914.511538 518
 
2.4%
16924.16365 508
 
2.4%
25980.08593 400
 
1.9%
22934.75192 374
 
1.8%
79411.69048 342
 
1.6%
6907.249522 340
 
1.6%
7826.339857 297
 
1.4%
11774.10017 296
 
1.4%
25684.75625 280
 
1.3%
Other values (389) 17261
81.2%
ValueCountFrequency (%)
339.9166667 5
< 0.1%
1630.969697 4
< 0.1%
1984.207048 1
 
< 0.1%
2142.314286 5
< 0.1%
2231.87619 2
 
< 0.1%
2240.15142 1
 
< 0.1%
2541.47644 1
 
< 0.1%
2730.433333 4
< 0.1%
2753.428205 3
< 0.1%
2832.758621 2
 
< 0.1%
ValueCountFrequency (%)
127523.4653 22
 
0.1%
122057.8909 3
 
< 0.1%
116979.5859 5
 
< 0.1%
114678.3262 91
0.4%
112053.3811 34
 
0.2%
111884.0976 51
0.2%
110451.2149 26
 
0.1%
103006.632 35
 
0.2%
95345.98438 3
 
< 0.1%
92106.09091 1
 
< 0.1%

Interactions

2023-05-04T23:31:06.188460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:34.253731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:36.314875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:38.322598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:40.404151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:42.554815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:44.517987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:46.950161image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:49.005221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:51.049638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:53.106757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:55.074779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:57.227834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:59.303274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:01.497651image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:04.060652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:06.314096image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:34.387593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:36.435588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:38.443456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:40.540308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:42.675280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:44.652548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:47.076832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:49.123460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:51.180501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:53.229034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:55.206017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:57.363989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:59.436500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:01.620928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:04.188823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:06.446191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:34.506498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:36.555665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:38.567250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:40.667155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:42.794391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:44.802736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:47.199670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:49.241703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:51.306537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:53.348722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:55.337669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:57.494968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:59.570407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:01.737669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:04.324968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:06.570346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:34.621828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:36.667589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:38.692817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:40.789418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:42.911584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:45.327832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:47.313910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:49.350734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:51.429847image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:53.463726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:55.464367image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:57.613895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:59.691971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:01.848963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:04.450959image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:06.713422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:34.754084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:36.798700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:38.821474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:40.925649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:43.043851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:45.462154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:47.447141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:49.479587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:51.564766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:53.590905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:55.611591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:57.748760image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:59.832107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:01.982902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:04.596669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:06.837602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:34.872293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:36.924771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:38.942280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:41.053501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:43.159576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:45.580437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:47.573964image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:49.595677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:51.693375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:53.712821image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:55.733151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:57.877243image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:59.967263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:02.100905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:04.718533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:06.969835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:34.995484image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:37.045280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:39.060582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:41.182605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:43.282002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:45.708609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:47.698219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:49.714877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:51.818089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:53.830890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:55.863900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:58.003721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:00.131018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:02.225121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:04.851765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:07.101574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:35.119777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:37.173794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:39.181875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:41.319343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:43.400494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:45.829719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:47.844709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:49.842189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:51.945584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:53.970275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:55.993075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:58.128137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:00.267246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:02.413645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:04.976906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:07.221733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:35.233400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:37.285816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:39.318027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:41.446043image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:43.515762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:45.944933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:47.967479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:49.960828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:52.063133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:54.082519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:56.116781image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:58.251486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:00.395093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:02.525856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:05.098625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:07.368782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:35.373542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:37.428949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:39.476636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:41.581197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:43.643461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-04T23:30:54.211171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-04T23:31:07.489635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:35.507694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:37.554644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:39.637756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:41.729320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:43.769253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-04T23:30:52.445049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:54.456575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:56.525629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:58.650900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:00.807079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:02.896154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:05.508425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:07.770623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:35.773757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:37.808559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-04T23:30:56.933840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:59.034441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-04T23:31:03.298957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-04T23:30:38.188453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-04T23:30:42.420124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-04T23:30:57.089678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:30:59.172105image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:01.357493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:03.422212image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T23:31:06.034736image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-04T23:31:14.578350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
사망인명피해수화재발생일자화재발생시현장소방서거리현장안전센터거리현장소방지역대거리시간단위기온시간단위풍속시간단위풍향시간단위습도시간단위가시거리재산피해액평균 상대습도(%)실효습도전기사용량(KWh)가스사용량(KWh)분기구분발화요인대분류명시설장소중분류명방화관리대상여부
사망인명피해수1.0000.017-0.0300.0070.0060.000-0.024-0.012-0.005-0.0030.0040.214-0.012-0.009-0.0060.0040.0000.1480.0240.019
화재발생일자0.0171.0000.022-0.024-0.0220.0650.0420.077-0.0810.1570.2060.0010.2210.279-0.032-0.0220.2640.0460.0300.017
화재발생시-0.0300.0221.000-0.0010.0020.0120.1180.2080.159-0.1690.106-0.0610.0050.015-0.0000.0010.0240.0360.0320.029
현장소방서거리0.007-0.024-0.0011.0000.218-0.002-0.011-0.001-0.008-0.007-0.0060.016-0.016-0.021-0.015-0.0810.0040.0150.0400.034
현장안전센터거리0.006-0.0220.0020.2181.000-0.009-0.0120.0150.010-0.0180.0100.026-0.012-0.0140.0860.0530.0090.0000.0700.020
현장소방지역대거리0.0000.0650.012-0.002-0.0091.0000.0010.0130.0080.0100.0210.0030.0150.0210.0190.0110.0100.0040.0000.000
시간단위기온-0.0240.0420.118-0.011-0.0120.0011.0000.018-0.1540.1750.013-0.0740.4030.491-0.007-0.0130.5420.0310.0140.022
시간단위풍속-0.0120.0770.208-0.0010.0150.0130.0181.0000.247-0.2540.1630.027-0.081-0.0360.0170.0080.0810.0040.0000.000
시간단위풍향-0.005-0.0810.159-0.0080.0100.008-0.1540.2471.000-0.2490.0340.001-0.234-0.1780.0060.0030.1660.0180.0000.000
시간단위습도-0.0030.157-0.169-0.007-0.0180.0100.175-0.254-0.2491.000-0.391-0.0300.7610.649-0.015-0.0080.1990.0470.0160.000
시간단위가시거리0.0040.2060.106-0.0060.0100.0210.0130.1630.034-0.3911.000-0.000-0.297-0.1410.0080.0060.0730.0170.0000.000
재산피해액0.2140.001-0.0610.0160.0260.003-0.0740.0270.001-0.030-0.0001.000-0.043-0.0440.0570.0350.0000.0040.0740.011
평균 상대습도(%)-0.0120.2210.005-0.016-0.0120.0150.403-0.081-0.2340.761-0.297-0.0431.0000.848-0.018-0.0110.2610.0450.0150.000
실효습도-0.0090.2790.015-0.021-0.0140.0210.491-0.036-0.1780.649-0.141-0.0440.8481.000-0.020-0.0160.3380.0450.0090.008
전기사용량(KWh)-0.006-0.032-0.000-0.0150.0860.019-0.0070.0170.006-0.0150.0080.057-0.018-0.0201.0000.7310.0030.0180.0940.249
가스사용량(KWh)0.004-0.0220.001-0.0810.0530.011-0.0130.0080.003-0.0080.0060.035-0.011-0.0160.7311.0000.0000.0210.0630.192
분기구분0.0000.2640.0240.0040.0090.0100.5420.0810.1660.1990.0730.0000.2610.3380.0030.0001.0000.0610.0290.008
발화요인대분류명0.1480.0460.0360.0150.0000.0040.0310.0040.0180.0470.0170.0040.0450.0450.0180.0210.0611.0000.0910.048
시설장소중분류명0.0240.0300.0320.0400.0700.0000.0140.0000.0000.0160.0000.0740.0150.0090.0940.0630.0290.0911.0000.408
방화관리대상여부0.0190.0170.0290.0340.0200.0000.0220.0000.0000.0000.0000.0110.0000.0080.2490.1920.0080.0480.4081.000

Missing values

2023-05-04T23:31:08.397183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-04T23:31:09.023371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

사망인명피해수분기구분화재발생일자화재발생시현장소방서거리현장안전센터거리현장소방지역대거리발화요인대분류명시설장소중분류명방화관리대상여부시간단위기온시간단위풍속시간단위풍향시간단위습도시간단위가시거리재산피해액평균 상대습도(%)실효습도주소전기사용량(KWh)가스사용량(KWh)
001201701010430.0전기적 요인단독주택Y0.22.220.079.04911339175.954.273750강남구 논현동18096.21046311774.10017
1012017010715420.0부주의일상서비스N9.31.9270.051.07033564.949.554054강남구 논현동18096.21046311774.10017
2012017011520510.0부주의음식점N-4.21.8250.043.0200030835.331.607682강남구 논현동18096.21046311774.10017
3012017012010410.0부주의음식점Y-4.44.5270.075.055818470.350.355768강남구 논현동18096.21046311774.10017
401201701273530.0부주의단독주택N1.55.9270.082.063487747.343.742292강남구 논현동18096.21046311774.10017
501201701281540.0기계적 요인음식점Y-6.12.6270.052.0200013538.039.579228강남구 논현동18096.21046311774.10017
6012017020322520.0부주의일상서비스N0.60.10.077.052610565.446.303062강남구 논현동18096.21046311774.10017
7012017021115420.0부주의음식점N-0.14.2270.033.0197542245.438.257452강남구 논현동18096.21046311774.10017
801201702217410.0미상기타건축물N-7.50.40.052.01965731537.141.605767강남구 논현동18096.21046311774.10017
901201702279620.0미상의료시설N1.01.320.043.0178235337.440.998462강남구 논현동18096.21046311774.10017
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